International journal of women's health · 2026
Integrated Genomic Analysis Reveals New Diagnostic Biomarkers and Immune Mechanisms for Polycystic Ovary Syndrome.
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Huang Ning, Lou LuYun
Аннотация
OBJECTIVE: Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder associated with metabolic dysregulation and chronic inflammation. This study employed bioinformatics approaches to analyze the roles of neutrophil extracellular traps (NETs)-related genes (NETRGs) and mitochondria-related genes (MRGs) in PCOS pathogenesis.
METHODS: Through differential expression analysis, enrichment studies, and machine learning models, we identified 18 differentially expressed neutrophil extracellular trap- and mitophagy-related genes (NETMRDEGs), such as S100A9, MYH9, and ATG5. Functional enrichment revealed their involvement in IL-17 signaling and neutrophil chemotaxis. A LASSO regression model incorporating these genes demonstrated high diagnostic accuracy (AUC > 0.9). Immune infiltration analysis highlighted activated B cells and effector memory CD8+ T cells as key contributors. Moreover, protein-protein interaction (PPI) networks further elucidated functional synergies among NETMRDEGs.
RESULTS: The study identified distinct molecular clusters in PCOS patients based on the expression of NETMRDEGs. Cluster 2 exhibited higher immune infiltration (eg, gamma delta T cells and eosinophils) and severe metabolic dysfunction. The LASSO model achieved superior diagnostic performance (AUC: 0.93) compared to traditional biomarkers such as testosterone (AUC: 0.68). Experimental validation in a PCOS mouse model confirmed elevated expression of hub genes (S100A9, MYH9, and ATG5) and increased granulosa cell apoptosis.
CONCLUSION: The interaction between NETRGs and MRGs forms a "dual-engine" mechanism driving PCOS pathogenesis. This study proposed a novel diagnostic model and therapeutic targets (eg, DNase I and urolithin A), advancing precision medicine for PCOS.
METHODS: Through differential expression analysis, enrichment studies, and machine learning models, we identified 18 differentially expressed neutrophil extracellular trap- and mitophagy-related genes (NETMRDEGs), such as S100A9, MYH9, and ATG5. Functional enrichment revealed their involvement in IL-17 signaling and neutrophil chemotaxis. A LASSO regression model incorporating these genes demonstrated high diagnostic accuracy (AUC > 0.9). Immune infiltration analysis highlighted activated B cells and effector memory CD8+ T cells as key contributors. Moreover, protein-protein interaction (PPI) networks further elucidated functional synergies among NETMRDEGs.
RESULTS: The study identified distinct molecular clusters in PCOS patients based on the expression of NETMRDEGs. Cluster 2 exhibited higher immune infiltration (eg, gamma delta T cells and eosinophils) and severe metabolic dysfunction. The LASSO model achieved superior diagnostic performance (AUC: 0.93) compared to traditional biomarkers such as testosterone (AUC: 0.68). Experimental validation in a PCOS mouse model confirmed elevated expression of hub genes (S100A9, MYH9, and ATG5) and increased granulosa cell apoptosis.
CONCLUSION: The interaction between NETRGs and MRGs forms a "dual-engine" mechanism driving PCOS pathogenesis. This study proposed a novel diagnostic model and therapeutic targets (eg, DNase I and urolithin A), advancing precision medicine for PCOS.